rag-chunking-strategy

Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking

509 stars

Best use case

rag-chunking-strategy is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking

Teams using rag-chunking-strategy should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/rag-chunking-strategy/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/ai-agents-conversational/skills/rag-chunking-strategy/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/rag-chunking-strategy/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How rag-chunking-strategy Compares

Feature / Agentrag-chunking-strategyStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# RAG Chunking Strategy Skill

## Capabilities

- Implement multiple document chunking strategies
- Configure semantic chunking based on content boundaries
- Set up recursive character text splitting
- Design fixed-size chunking with overlap
- Implement document-aware chunking (markdown, code, etc.)
- Optimize chunk sizes for retrieval quality

## Target Processes

- rag-pipeline-implementation
- chunking-strategy-design

## Implementation Details

### Chunking Strategies

1. **RecursiveCharacterTextSplitter**: Hierarchical splitting with separators
2. **SemanticChunker**: Embedding-based semantic boundaries
3. **TokenTextSplitter**: Token-aware splitting
4. **MarkdownHeaderTextSplitter**: Structure-aware markdown splitting
5. **CodeSplitter**: Language-aware code chunking

### Configuration Options

- Chunk size (characters or tokens)
- Chunk overlap percentage
- Separator hierarchy
- Embedding model for semantic chunking
- Document type detection

### Best Practices

- Match chunk size to embedding model limits
- Use appropriate overlap for context preservation
- Test retrieval quality with different strategies
- Consider document structure in strategy selection

### Dependencies

- langchain-text-splitters
- sentence-transformers (for semantic chunking)

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